#Introduction for working code:

  1. Download the Code from Github. The new downloaded folder hc_switch_cccu-main contains the R project, Rmd Code, created data frames, knitted Html Code, Figures as .pdf and.jpg, Preregistered synthetic Code, and a folder to store the downloaded RData called RData.

Name the folder appropriately (when downloading the code from GitHub the folder will be hc_switch_cccu-main).

  1. Download data from https://www.icpsr.umich.edu/web/DSDR/studies/37067/datadocumentation

Attention! The downloaded project from GitHub does not contain any data files!

–> Click on Download, then select R, then follow the login –> store data in the folder specific to the R project in an extra sub-folder called RData (i.e., hc_switch_cccu-main - > RData). If you downloaded the code from GitHub, the RData folder will be already in the hc_switch_cccu-main projects folder.

–> Within the RData folder store data for wave 1 in DS0001, for wave 2 in DS0002 and so on (i.e., hc_switch_cccu-main - > RData -> DS0001 ). If you downloaded the code from GitHub, the relevant folders will be already there.

–> name the Rdata files according to the wave:

Wave 1: 37067-0001-Data (i.e., hc_switch_cccu-main - > RData -> DS0001 -> 37067-0001-Data.rda )

Wave 2: 37067-0002-Data (i.e., hc_switch_cccu-main - > RData -> DS0002 -> 37067-0002-Data.rda )

Wave 3: 37067-0003-Data (i.e., hc_switch_cccu-main - > RData -> DS0003 -> 37067-0003-Data.rda )

Wave 4: 37067-0004-Data (i.e., hc_switch_cccu-main - > RData -> DS0004 -> 37067-0004-Data.rda )

–> attention to the .rda ending when loading the data into R!

  1. make sure to install all relevant packages before running the code!

All relevant packages and versions can be seen in the section “Analyses”

  1. run the code according to the defined order beginning with 01_ (or 0 if you want to have a look at the synthetic data) and ending with 09_ so that all data frames are created appropriately

  2. Run the Code :) # Codebook {.tabset} ## 1. Packages and df

library(ggplot2)
theme_set(theme_bw())
library(codebook)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.2.1
## ✔ purrr     1.0.1     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(future)
library(psych)
## 
## Attache Paket: 'psych'
## 
## Das folgende Objekt ist maskiert 'package:codebook':
## 
##     bfi
## 
## Die folgenden Objekte sind maskiert von 'package:ggplot2':
## 
##     %+%, alpha
library(Hmisc)
## 
## Attache Paket: 'Hmisc'
## 
## Das folgende Objekt ist maskiert 'package:psych':
## 
##     describe
## 
## Die folgenden Objekte sind maskiert von 'package:dplyr':
## 
##     src, summarize
## 
## Die folgenden Objekte sind maskiert von 'package:base':
## 
##     format.pval, units
load("cccu_MA.RData")

2. Select data

cccu_MA = cccu_MA %>%
  select(WAVE, switch, hc, contra_satis, hc_dur, sexual_satisfaction, sex_freq,
         AGE, DEGREE_recode, POVRATE, HLTHPROB_recode, MEDPROB_recode, GAPINS_recode, TYPEINS_recode, NKIDS_t1, pregnant_between_waves, had_baby_between_waves, Avoid_r, FEELPG_recode, rel_dur_factor)

3. Codebook

codebook(cccu_MA, missingness_report = FALSE, indent = "###")

Metadata

Description

Dataset name: cccu_MA

The dataset has N=3787 rows and 20 columns. 2285 rows have no missing values on any column.

Metadata for search engines
  • Date published: 2023-11-14
x
WAVE
switch
hc
contra_satis
hc_dur
sexual_satisfaction
sex_freq
AGE
DEGREE_recode
POVRATE
HLTHPROB_recode
MEDPROB_recode
GAPINS_recode
TYPEINS_recode
NKIDS_t1
pregnant_between_waves
had_baby_between_waves
Avoid_r
FEELPG_recode
rel_dur_factor

###Variables

WAVE
Distribution
Distribution of values for WAVE

Distribution of values for WAVE

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
WAVE factor FALSE 1. t1,
2. t2,
3. t3,
4. t4		 0 1 3 t1: 1723, t2: 1181, t3: 883, t4: 0 NA
switch
Distribution
Distribution of values for switch

Distribution of values for switch

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
switch numeric 0 1 0 0 1 0.1280697 0.3342115 ▇▁▁▁▁ NA
hc
Distribution
Distribution of values for hc

Distribution of values for hc

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
hc factor FALSE 1. hc,
2. non_hc,
3. IUD&non_hc		 0 1 2 hc: 2294, non: 1493, IUD: 0 NA
contra_satis
Distribution
Distribution of values for contra_satis

Distribution of values for contra_satis

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
contra_satis numeric 0 1 1 4 4 3.486137 0.7016738 ▁▁▁▅▇ NA
hc_dur
Distribution
Distribution of values for hc_dur

Distribution of values for hc_dur

1502 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
hc_dur numeric 1502 0.60338 0 10 41 10.98993 6.753113 ▇▇▃▁▁ NA
sexual_satisfaction
Distribution
Distribution of values for sexual_satisfaction

Distribution of values for sexual_satisfaction

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
sexual_satisfaction numeric 0 1 1 5 6 5.016636 1.156752 ▁▁▃▅▇ NA
sex_freq
Distribution
Distribution of values for sex_freq

Distribution of values for sex_freq

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
sex_freq factor FALSE 1. No sex or once,
2. 2-5 times,
3. 6-10 times,
4. 11 or more times		 0 1 4 2-5: 1659, 6-1: 1036, 11 : 594, No : 498 NA
AGE
Distribution
Distribution of values for AGE

Distribution of values for AGE

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
AGE numeric 0 1 18 27 39 28.15289 5.209466 ▃▇▆▅▅ NA
DEGREE_recode
Distribution
Distribution of values for DEGREE_recode

Distribution of values for DEGREE_recode

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
DEGREE_recode factor FALSE 1. no formal education,
2. 1st to 4th grade,
3. 5th/6th grade,
4. 7th/8th grade,
5. 9th grade,
6. 10th grade,
7. 11th grade,
8. 12th grade / no diploma,
9. high school diploma or equivalent,
10. some college, no degree,
11. associate degree,
12. bachelors degree,
13. masters degree,
14. professional or doctoral degree		 0 1 13 bac: 1364, som: 924, mas: 442, hig: 421 NA
POVRATE
Distribution
Distribution of values for POVRATE

Distribution of values for POVRATE

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
POVRATE numeric 0 1 14 282 1532 320.1458 231.8161 ▇▅▁▁▁ NA
HLTHPROB_recode
Distribution
Distribution of values for HLTHPROB_recode

Distribution of values for HLTHPROB_recode

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
HLTHPROB_recode factor FALSE 1. No,
2. Yes		 0 1 2 No: 3125, Yes: 662 NA
MEDPROB_recode
Distribution
Distribution of values for MEDPROB_recode

Distribution of values for MEDPROB_recode

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
MEDPROB_recode factor FALSE 1. No,
2. Yes		 0 1 2 No: 3444, Yes: 343 NA
GAPINS_recode
Distribution
Distribution of values for GAPINS_recode

Distribution of values for GAPINS_recode

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
GAPINS_recode factor FALSE 1. No,
2. Yes		 0 1 2 No: 3076, Yes: 711 NA
TYPEINS_recode
Distribution
Distribution of values for TYPEINS_recode

Distribution of values for TYPEINS_recode

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
TYPEINS_recode factor FALSE 1. No,
2. Yes		 0 1 2 Yes: 3218, No: 569 NA
NKIDS_t1
Distribution
Distribution of values for NKIDS_t1

Distribution of values for NKIDS_t1

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
NKIDS_t1 factor FALSE 1. 0,
2. 1,
3. 2,
4. 3,
5. 4 or more		 0 1 5 0: 2121, 1: 704, 2: 631, 3: 216 NA
pregnant_between_waves
Distribution
Distribution of values for pregnant_between_waves

Distribution of values for pregnant_between_waves

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
pregnant_between_waves factor FALSE 1. No,
2. Yes		 0 1 2 No: 3655, Yes: 132 NA
had_baby_between_waves
Distribution
Distribution of values for had_baby_between_waves

Distribution of values for had_baby_between_waves

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
had_baby_between_waves factor FALSE 1. No,
2. Yes		 0 1 2 No: 3697, Yes: 90 NA
Avoid_r
Distribution
Distribution of values for Avoid_r

Distribution of values for Avoid_r

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
Avoid_r numeric 0 1 1 5 6 4.777396 1.55302 ▂▂▂▃▇ NA
FEELPG_recode
Distribution
Distribution of values for FEELPG_recode

Distribution of values for FEELPG_recode

0 missing values.

Summary statistics
name data_type n_missing complete_rate min median max mean sd hist label
FEELPG_recode numeric 0 1 1 3 6 3.40507 1.689799 ▇▃▅▃▃ NA
rel_dur_factor
Distribution
Distribution of values for rel_dur_factor

Distribution of values for rel_dur_factor

0 missing values.

Summary statistics
name data_type ordered value_labels n_missing complete_rate n_unique top_counts label
rel_dur_factor factor FALSE 1. Single,
2. 0q-25q,
3. 26q-50q,
4. 51q-75q,
5. 76q-100q		 0 1 5 Sin: 932, 0q-: 792, 26q: 760, 51q: 684 NA

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "cccu_MA",
  "datePublished": "2023-11-14",
  "description": "The dataset has N=3787 rows and 20 columns.\n2285 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                   |label | n_missing|\n|:----------------------|:-----|---------:|\n|WAVE                   |NA    |         0|\n|switch                 |NA    |         0|\n|hc                     |NA    |         0|\n|contra_satis           |NA    |         0|\n|hc_dur                 |NA    |      1502|\n|sexual_satisfaction    |NA    |         0|\n|sex_freq               |NA    |         0|\n|AGE                    |NA    |         0|\n|DEGREE_recode          |NA    |         0|\n|POVRATE                |NA    |         0|\n|HLTHPROB_recode        |NA    |         0|\n|MEDPROB_recode         |NA    |         0|\n|GAPINS_recode          |NA    |         0|\n|TYPEINS_recode         |NA    |         0|\n|NKIDS_t1               |NA    |         0|\n|pregnant_between_waves |NA    |         0|\n|had_baby_between_waves |NA    |         0|\n|Avoid_r                |NA    |         0|\n|FEELPG_recode          |NA    |         0|\n|rel_dur_factor         |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["WAVE", "switch", "hc", "contra_satis", "hc_dur", "sexual_satisfaction", "sex_freq", "AGE", "DEGREE_recode", "POVRATE", "HLTHPROB_recode", "MEDPROB_recode", "GAPINS_recode", "TYPEINS_recode", "NKIDS_t1", "pregnant_between_waves", "had_baby_between_waves", "Avoid_r", "FEELPG_recode", "rel_dur_factor"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "WAVE",
      "value": "1. t1,\n2. t2,\n3. t3,\n4. t4",
      "@type": "propertyValue"
    },
    {
      "name": "switch",
      "@type": "propertyValue"
    },
    {
      "name": "hc",
      "value": "1. hc,\n2. non_hc,\n3. IUD&non_hc",
      "@type": "propertyValue"
    },
    {
      "name": "contra_satis",
      "@type": "propertyValue"
    },
    {
      "name": "hc_dur",
      "@type": "propertyValue"
    },
    {
      "name": "sexual_satisfaction",
      "@type": "propertyValue"
    },
    {
      "name": "sex_freq",
      "value": "1. No sex or once,\n2. 2-5 times,\n3. 6-10 times,\n4. 11 or more times",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "DEGREE_recode",
      "value": "1. no formal education,\n2. 1st to 4th grade,\n3. 5th/6th grade,\n4. 7th/8th grade,\n5. 9th grade,\n6. 10th grade,\n7. 11th grade,\n8. 12th grade / no diploma,\n9. high school diploma or equivalent,\n10. some college, no degree,\n11. associate degree,\n12. bachelors degree,\n13. masters degree,\n14. professional or doctoral degree",
      "@type": "propertyValue"
    },
    {
      "name": "POVRATE",
      "@type": "propertyValue"
    },
    {
      "name": "HLTHPROB_recode",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "MEDPROB_recode",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "GAPINS_recode",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "TYPEINS_recode",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "NKIDS_t1",
      "value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4 or more",
      "@type": "propertyValue"
    },
    {
      "name": "pregnant_between_waves",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "had_baby_between_waves",
      "value": "1. No,\n2. Yes",
      "@type": "propertyValue"
    },
    {
      "name": "Avoid_r",
      "@type": "propertyValue"
    },
    {
      "name": "FEELPG_recode",
      "@type": "propertyValue"
    },
    {
      "name": "rel_dur_factor",
      "value": "1. Single,\n2. 0q-25q,\n3. 26q-50q,\n4. 51q-75q,\n5. 76q-100q",
      "@type": "propertyValue"
    }
  ]
}`

#Plots

#Correlations

#for t1
cccu_MA_t1 <- cccu_MA%>%
  filter(WAVE == "t1")
cccu_MA_t1_correlations <- cccu_MA_t1 %>%
  select(contra_satis, hc_dur,
    sexual_satisfaction,
    AGE, POVRATE, Avoid_r, FEELPG_recode)


pairs.panels(cccu_MA_t1_correlations, cex.cor = 10)

rcorr(as.matrix(cccu_MA_t1_correlations))
##                     contra_satis hc_dur sexual_satisfaction   AGE POVRATE
## contra_satis                1.00   0.15                0.16  0.02    0.06
## hc_dur                      0.15   1.00                0.01  0.25    0.15
## sexual_satisfaction         0.16   0.01                1.00 -0.03   -0.01
## AGE                         0.02   0.25               -0.03  1.00    0.18
## POVRATE                     0.06   0.15               -0.01  0.18    1.00
## Avoid_r                     0.13   0.02               -0.03 -0.14   -0.02
## FEELPG_recode              -0.06  -0.03                0.12  0.20   -0.04
##                     Avoid_r FEELPG_recode
## contra_satis           0.13         -0.06
## hc_dur                 0.02         -0.03
## sexual_satisfaction   -0.03          0.12
## AGE                   -0.14          0.20
## POVRATE               -0.02         -0.04
## Avoid_r                1.00         -0.61
## FEELPG_recode         -0.61          1.00
## 
## n
##                     contra_satis hc_dur sexual_satisfaction  AGE POVRATE
## contra_satis                1723   1032                1723 1723    1723
## hc_dur                      1032   1032                1032 1032    1032
## sexual_satisfaction         1723   1032                1723 1723    1723
## AGE                         1723   1032                1723 1723    1723
## POVRATE                     1723   1032                1723 1723    1723
## Avoid_r                     1723   1032                1723 1723    1723
## FEELPG_recode               1723   1032                1723 1723    1723
##                     Avoid_r FEELPG_recode
## contra_satis           1723          1723
## hc_dur                 1032          1032
## sexual_satisfaction    1723          1723
## AGE                    1723          1723
## POVRATE                1723          1723
## Avoid_r                1723          1723
## FEELPG_recode          1723          1723
## 
## P
##                     contra_satis hc_dur sexual_satisfaction AGE    POVRATE
## contra_satis                     0.0000 0.0000              0.4981 0.0177 
## hc_dur              0.0000              0.8285              0.0000 0.0000 
## sexual_satisfaction 0.0000       0.8285                     0.1895 0.6176 
## AGE                 0.4981       0.0000 0.1895                     0.0000 
## POVRATE             0.0177       0.0000 0.6176              0.0000        
## Avoid_r             0.0000       0.5793 0.1834              0.0000 0.4026 
## FEELPG_recode       0.0084       0.2867 0.0000              0.0000 0.1272 
##                     Avoid_r FEELPG_recode
## contra_satis        0.0000  0.0084       
## hc_dur              0.5793  0.2867       
## sexual_satisfaction 0.1834  0.0000       
## AGE                 0.0000  0.0000       
## POVRATE             0.4026  0.1272       
## Avoid_r                     0.0000       
## FEELPG_recode       0.0000
#for t2
cccu_MA_t2 <- cccu_MA%>%
  filter(WAVE == "t2")

cccu_MA_t2_correlations <- cccu_MA_t2 %>%
  select(contra_satis, hc_dur,
    sexual_satisfaction,
    AGE, POVRATE, Avoid_r, FEELPG_recode)
pairs.panels(cccu_MA_t2_correlations, cex.cor = 10)

rcorr(as.matrix(cccu_MA_t2_correlations))
##                     contra_satis hc_dur sexual_satisfaction   AGE POVRATE
## contra_satis                1.00   0.15                0.14  0.01    0.06
## hc_dur                      0.15   1.00                0.04  0.20    0.16
## sexual_satisfaction         0.14   0.04                1.00 -0.05   -0.01
## AGE                         0.01   0.20               -0.05  1.00    0.16
## POVRATE                     0.06   0.16               -0.01  0.16    1.00
## Avoid_r                     0.04   0.05               -0.08 -0.14    0.00
## FEELPG_recode               0.00   0.01                0.18  0.19   -0.04
##                     Avoid_r FEELPG_recode
## contra_satis           0.04          0.00
## hc_dur                 0.05          0.01
## sexual_satisfaction   -0.08          0.18
## AGE                   -0.14          0.19
## POVRATE                0.00         -0.04
## Avoid_r                1.00         -0.62
## FEELPG_recode         -0.62          1.00
## 
## n
##                     contra_satis hc_dur sexual_satisfaction  AGE POVRATE
## contra_satis                1181    717                1181 1181    1181
## hc_dur                       717    717                 717  717     717
## sexual_satisfaction         1181    717                1181 1181    1181
## AGE                         1181    717                1181 1181    1181
## POVRATE                     1181    717                1181 1181    1181
## Avoid_r                     1181    717                1181 1181    1181
## FEELPG_recode               1181    717                1181 1181    1181
##                     Avoid_r FEELPG_recode
## contra_satis           1181          1181
## hc_dur                  717           717
## sexual_satisfaction    1181          1181
## AGE                    1181          1181
## POVRATE                1181          1181
## Avoid_r                1181          1181
## FEELPG_recode          1181          1181
## 
## P
##                     contra_satis hc_dur sexual_satisfaction AGE    POVRATE
## contra_satis                     0.0000 0.0000              0.6556 0.0531 
## hc_dur              0.0000              0.2944              0.0000 0.0000 
## sexual_satisfaction 0.0000       0.2944                     0.1034 0.7755 
## AGE                 0.6556       0.0000 0.1034                     0.0000 
## POVRATE             0.0531       0.0000 0.7755              0.0000        
## Avoid_r             0.1471       0.1944 0.0098              0.0000 0.9078 
## FEELPG_recode       0.8963       0.8778 0.0000              0.0000 0.2005 
##                     Avoid_r FEELPG_recode
## contra_satis        0.1471  0.8963       
## hc_dur              0.1944  0.8778       
## sexual_satisfaction 0.0098  0.0000       
## AGE                 0.0000  0.0000       
## POVRATE             0.9078  0.2005       
## Avoid_r                     0.0000       
## FEELPG_recode       0.0000
#for t3
cccu_MA_t3 <- cccu_MA%>%
  filter(WAVE == "t3")

cccu_MA_t3_correlations <- cccu_MA_t3 %>%
  select(contra_satis, hc_dur,
    sexual_satisfaction,
    AGE, POVRATE, Avoid_r, FEELPG_recode)

pairs.panels(cccu_MA_t3_correlations, cex.cor = 10)

rcorr(as.matrix(cccu_MA_t3_correlations))
##                     contra_satis hc_dur sexual_satisfaction   AGE POVRATE
## contra_satis                1.00   0.17                0.14 -0.02    0.06
## hc_dur                      0.17   1.00                0.03  0.11    0.12
## sexual_satisfaction         0.14   0.03                1.00 -0.01   -0.02
## AGE                        -0.02   0.11               -0.01  1.00    0.16
## POVRATE                     0.06   0.12               -0.02  0.16    1.00
## Avoid_r                     0.05   0.01               -0.07 -0.12   -0.02
## FEELPG_recode               0.01  -0.02                0.16  0.16   -0.06
##                     Avoid_r FEELPG_recode
## contra_satis           0.05          0.01
## hc_dur                 0.01         -0.02
## sexual_satisfaction   -0.07          0.16
## AGE                   -0.12          0.16
## POVRATE               -0.02         -0.06
## Avoid_r                1.00         -0.64
## FEELPG_recode         -0.64          1.00
## 
## n
##                     contra_satis hc_dur sexual_satisfaction AGE POVRATE Avoid_r
## contra_satis                 883    536                 883 883     883     883
## hc_dur                       536    536                 536 536     536     536
## sexual_satisfaction          883    536                 883 883     883     883
## AGE                          883    536                 883 883     883     883
## POVRATE                      883    536                 883 883     883     883
## Avoid_r                      883    536                 883 883     883     883
## FEELPG_recode                883    536                 883 883     883     883
##                     FEELPG_recode
## contra_satis                  883
## hc_dur                        536
## sexual_satisfaction           883
## AGE                           883
## POVRATE                       883
## Avoid_r                       883
## FEELPG_recode                 883
## 
## P
##                     contra_satis hc_dur sexual_satisfaction AGE    POVRATE
## contra_satis                     0.0000 0.0000              0.6369 0.0580 
## hc_dur              0.0000              0.4937              0.0095 0.0059 
## sexual_satisfaction 0.0000       0.4937                     0.8171 0.5747 
## AGE                 0.6369       0.0095 0.8171                     0.0000 
## POVRATE             0.0580       0.0059 0.5747              0.0000        
## Avoid_r             0.1777       0.9037 0.0373              0.0004 0.5264 
## FEELPG_recode       0.7616       0.6797 0.0000              0.0000 0.0715 
##                     Avoid_r FEELPG_recode
## contra_satis        0.1777  0.7616       
## hc_dur              0.9037  0.6797       
## sexual_satisfaction 0.0373  0.0000       
## AGE                 0.0004  0.0000       
## POVRATE             0.5264  0.0715       
## Avoid_r                     0.0000       
## FEELPG_recode       0.0000